Abstract [en]

Boarder security is usually based on observing and analyzing the movement of MovingPoint Objects (MPOs): vehicle, boats, pedestrian or aircraft for example. This movementanalysis can directly be made by an operator observing the MPOs in real-time, but theprocess is time-consuming and approximate. This is why the states of each MPO (ID, location,speed, direction) are sensed in real-time using Global Navigation Satellite System(GNSS), Automatic Identification System (AIS) and radar sensing, thus creating a streamof MPO states. This research work proposes and carries out (1) a method for detectingfour different moving point patterns based on this input stream (2) a comparison betweenthree possible implementations of the moving point pattern detectors based on three differentData Stream Management Systems (DSMS). Moving point patterns can be dividedin two groups: (1) individual location patterns are based on the analysis of the successivestates of one MPO, (2) set-based relative motion patterns are based on the analysis ofthe relative motion of groups of MPOs within a set. This research focuses on detectingfour moving point patterns: (1) the geofence pattern consists of one MPO enteringor exiting one of the predefined areas called geofences, (2) the track pattern consists ofone MPO following the same direction for a given number of time steps and satisfying agiven spatial constraint, (3) the flock pattern consists of a group of geographically closeMPOs following the same direction, (4) the leadership pattern consists of a track patternwith the corresponding MPO anticipating the direction of geographically close MPOs atthe last time step. The two first patterns are individual location patterns, while the othersare set-based relative motion patterns. This research work proposes a method for detectinggeofence patterns based on the update of a table storing the last sensed state of eachMPO. The approach used for detecting track, flock and leadership patterns is based on theupdate of a REMO matrix (RElative MOtion matrix) where rows correspond to MPOs,columns to time steps and cells record the direction of movement. For the detection offlock patterns a simple but effective probabilistic grid-based approach is proposed in orderto detect clusters of MPOs within the MPOs following the same direction: (1) the Filteringphase partitions the study area into square-shaped cells -according to the dimensionof the spatial constraint- and selects spatially contiguous grid cells called candidate areasthat potentially contain flock patterns (2) for each candidate area, the Refinement phasegenerates disks of the size of the spatial constraint within the selected area until one diskcontains enough MPOs, so that the corresponding MPOs are considered to build a flockpattern. The pattern detectors are implemented on three DSMSs presenting differentcharacteristics: Esri ArcGIS GeoEvent Extension for Server (GeoEvent Ext.), a workflow-based technology that ingests each MPO state separately, Apache Spark Streaming(Spark), a MapReduce-based technology that processes the input stream in batches in ahighly-parallel processing framework and Apache Flink (Flink), a hybrid technology thatingests the states separately but offers several MapReduce semantics. GeoEvent Ext. onlylends itself for a nature implementation of the geofence detector, while the other DSMSsaccommodate the implementation of all detectors. Therefore, the geofence, track, flockand leadership pattern detectors are implemented on Spark and Flink, and empiricallyevaluated in terms of scalability in time/space based on the variation of parameters characterizingthe patterns and/or the input stream. The results of the empirical evaluationshows that the implementation on Flink uses globally less computer resources than theone on Spark. Moreover, the program based on Flink is less sensitive to the variability ofparameters describing either the input stream or the patterns to be detected.